SoroushVahidi/scidocs huggingface dataset:
VisDB: database exploration using multidimensional visualization
(1994)

Discusses how the VisDB system supports the query specification process by representing the result visually. The main idea behind the system stems from the view of relational database tables as sets of multidimensional data where the number of attributes corresponds to the number of dimensions. In such a view, it is often unclear. In this system, each display pixel represents one database item. Pixels are arranged and colored to indicate the item's relevance to a user query and to give a visual impression of the resulting data set.<<ETX>>
A High-Performance Drive Circuit for All Solid-State Marx Generator
(2016)

In recent years, all solid-state Marx generators have been found more and more extensive applications in industrial, environmental, and biological fields. All solid-state Marx generators have many requirements for their drive circuits, such as good synchrony for driving signals at different stages, fine isolation between control signals and the main circuits, adjustable pulsewidths and frequencies, and good driving abilities. This paper proposes a high-performance drive circuit for a 24-stage Marx generator based on IGBTs. A half-bridge circuit using IR2110 outputs a high-current turning-on drive signal (positive) and a turning-off drive signal (negative) with adjustable dead time. The control drivers are input to the common primary side of 24 nanocrystalline magnetic transformers, which isolate the control circuit and the main circuit. Through gate circuits at the second sides of the magnetic cores, the turning-on drive signal charges 24 gate-emitter capacitors to required voltages and consequently all IGBTs move into on state until the turning-off signal arrives. Similarly, the negative turning-off drive signal charges all gate-emitter capacitors to a negative voltage which ensures all IGBTs stay in the off state. Therefore, the pulsewidth is determined by the phase difference between the turning-on and turning-off drive signals. Equipped with this drive circuit, the 24-stage Marx generator is able to produce stable high voltage pulse with a peak value of -9.6 kV, PRF $0.05\sim 5$ kHz, and different pulsewidths. In this paper, the design details and experimental confirmation of the proposed drive circuit are illustrated.
New classes of miniaturized planar Marchand baluns
(2005)

Three new classes of miniaturized Marchand balun are defined based on the synthesis of filter prototypes. They are suitable for mixed lumped-distributed planar realizations with small size resulting from transmission-line resonators being a quarter-wavelength long at frequencies higher than the passband center frequency. Each class corresponds to an S-plane bandpass prototype derived from the specification of transmission zero locations. A tunable 50:100-/spl Omega/ balun is realized at 1 GHz to demonstrate the advantages of the approach presented here.
A multichannel CSMA MAC protocol for multihop wireless networks
(1999)

We describe a new carrier–sense multiple access (CSMA) protocol for multihop wireless networks, sometimes also called ad hoc networks. The CSMA protocol divides the available bandwidth into several channels and selects an id le channel randomly for packet transmission. It also employs a notion of “soft” channel reservation as it gives preference to the channel that was used for the last successful transmission. We show via simulations that this multichannel CSMA protocol provides a higher throughput compared to its single channel counterpart by reducing the packet loss due to collisions. We also show that the use of channel reservation provides better performance than multichannel CSMA with purely random idle channel selection.
DemNet: A Convolutional Neural Network for the detection of Alzheimer's Disease and Mild Cognitive Impairment
(2016)

The early diagnosis of Alzheimer's Disease (AD) and its prodromal form, Mild Cognitive Impairment (MCI), has been the subject of extensive research in recent years. Some recent studies have shown promising results in the diagnosis of AD and MCI using structural Magnetic Resonance Imaging (MRI) scans. In this paper, we propose the use of a Convolutional Neural Network (CNN) in the detection of AD and MCI. In particular, we modified the 16-layered VGGNet for the 3-way classification of AD, MCI and Healthy Controls (HC) on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset achieving an overall accuracy of 91.85% and outperforming several classifiers from other studies.
Thoraco-omphalopagus conjoined twins in Chamois-coloured domestic goat kids.
(2012)

Conjoined twins have been observed in a wide range of mammalian and non-mammalian species; they are considered to be more common in bovine, less frequent in sheep and pig and extremely rare in horse and goat. A pair of female conjoined twins was delivered from a 2-year-old Chamois-coloured domestic goat. Post-mortem examination revealed two identical and symmetrical twins, fused from the manubrium sterni to the region just caudal to the umbilicus. The rib cages were conjoined in the ventral plane with a single set of pericardial, pleural and peritoneal cavities. Internal examination revealed the presence of a common diaphragm and a single enlarged liver. Within a single central pericardium, two malformed hearts were present. Reports on this type of congenital duplication in goats have not been found in the literature. Thoracopagus and thoraco-omphalopagus are the most common types of conjoined twins in human beings and are associated with the highest mortality because of the frequent incidence of complex cardiac anatomy.
Advertising and Consumers' Communications
(2013)

U recently, brand identities were built by firms via brand image advertising. However, the flourishing consumer communication weakened the firms’ grip on their brands. The interaction between advertising and consumer communications and their joint impact on brand identity is the focal point of this paper. We present a model in which consumer preference for functional attributes may correlate with the identity they desire to project of themselves. This correlation is known to the firm but not to the consumers. Both the firm and the consumers can communicate their desired brand identity, although the actual brand identity is determined endogeneously by the composition of consumers who purchase it (i.e., what types of people consume the brand). We find that sometimes the firm can strengthen the identity of its brand by refraining from advertising. This result is based on the following intermediate finding: advertising can diminish the endogeneous informativeness of consumer communications by making it one-sided. Furthermore, it turns out that refraining from brand image advertising may be optimal for the firm when the product is especially well positioned to create a strong identity—i.e., when consumer preferences for functional and self-expressive attributes are highly correlated.
Extreme visualization: squeezing a billion records into a million pixels
(2008)

Database searches are usually performed with query languages and form fill in templates, with results displayed in tabular lists. However, excitement is building around dynamic queries sliders and other graphical selectors for query specification, with results displayed by information visualization techniques. These filtering techniques have proven to be effective for many tasks in which visual presentations enable discovery of relationships, clusters, outliers, gaps, and other patterns. Scaling visual presentations from millions to billions of records will require collaborative research efforts in information visualization and database management to enable rapid aggregation, meaningful coordinated windows, and effective summary graphics. This paper describes current and proposed solutions (atomic, aggregated, and density plots) that facilitate sense-making for interactive visual exploration of billion record data sets.
Discovering interpretable geo-social communities for user behavior prediction
(2016)

Social community detection is a growing field of interest in the area of social network applications, and many approaches have been developed, including graph partitioning, latent space model, block model and spectral clustering. Most existing work purely focuses on network structure information which is, however, often sparse, noisy and lack of interpretability. To improve the accuracy and interpretability of community discovery, we propose to infer users' social communities by incorporating their spatiotemporal data and semantic information. Technically, we propose a unified probabilistic generative model, User-Community-Geo-Topic (UCGT), to simulate the generative process of communities as a result of network proximities, spatiotemporal co-occurrences and semantic similarity. With a well-designed multi-component model structure and a parallel inference implementation to leverage the power of multicores and clusters, our UCGT model is expressive while remaining efficient and scalable to growing large-scale geo-social networking data. We deploy UCGT to two application scenarios of user behavior predictions: check-in prediction and social interaction prediction. Extensive experiments on two large-scale geo-social networking datasets show that UCGT achieves better performance than existing state-of-the-art comparison methods.
Mobile Phone Use and Sleep Quality and Length in College Students
(2011)

Proper sleep length and quality are essential for physical and mental health and have found to be related to a variety of negative outcomes (Brown, Buboltz, &Soper, 2002). College is a time,a transition for individuals where they begin laying a foundation for their future and acquiring sufficient sleep is of great value. College students are recognized as one of the most sleep-deprived groups, but also, as one of the most technologically-oriented population. Due to this combination, college students’ sleep habits and mobile phone use habits havebegun to receiveattention. The purpose of this study was to examine the relationship between sleep quality/length and mobile phone use among college students. Three hundred and fifty college students voluntarily participated by completing the Sleep Quality Index (SQI), The Sleep Habits Survey,the Mobile Phone Problem Use Scale(MPPUS), the SMS Problem Use Scale (SMS-PUDQ) and the Mini IPIP. Results indicate that various aspects of mobile phone use such as problem mobile phone use, addictive text messaging, problematic texting, and pathological texting are related to sleep quality, but not sleep length. Additionally, extraverted individuals were found to engage in greater mobile phone use and problematic text messaging.
Energy Big Data Analytics and Security: Challenges and Opportunities
(2016)

The limited available fossil fuels and the call for sustainable environment have brought about new technologies for the high efficiency in the use of fossil fuels and introduction of renewable energy. Smart grid is an emerging technology that can fulfill such demands by incorporating advanced information and communications technology (ICT). The pervasive deployment of the advanced ICT, especially the smart metering, will generate big energy data in terms of volume, velocity, and variety. The generated big data can bring huge benefits to the better energy planning, efficient energy generation, and distribution. As such data involve end users' privacy and secure operation of the critical infrastructure, there will be new security issues. This paper is to survey and discuss new findings and developments in the existing big energy data analytics and security. Several taxonomies have been proposed to express the intriguing relationships of various variables in the field.
A Feature-Based, Robust, Hierarchical Algorithm for Registering Pairs of Images of the Curved Human Retina
(2002)

ÐThis paper describes a robust hierarchical algorithm for fully-automatic registration of a pair of images of the curved human retina photographed by a fundus microscope. Accurate registration is essential for mosaic synthesis, change detection, and design of computer-aided instrumentation. Central to the new algorithm is a 12-parameter interimage transformation derived by modeling the retina as a rigid quadratic surface with unknown parameters, imaged by an uncalibrated weak perspective camera. The parameters of this model are estimated by matching vascular landmarks extracted by an algorithm that recursively traces the blood vessel structure. The parameter estimation technique, which could be generalized to other applications, is a hierarchy of models and methods: an initial match set is pruned based on a zeroth order transformation estimated as the peak of a similarity-weighted histogram; a first order, affine transformation is estimated using the reduced match set and least-median of squares; and the final, second order, 12-parameter transformation is estimated using an M-estimator initialized from the first order estimate. This hierarchy makes the algorithm robust to unmatchable image features and mismatches between features caused by large interframe motions. Before final convergence of the M-estimator, feature positions are refined and the correspondence set is enhanced using normalized sum-of-squared differences matching of regions deformed by the emerging transformation. Experiments involving 3,000 image pairs (I; HPR Â I; HPR pixels) from 16 different healthy eyes were performed. Starting with as low as 20 percent overlap between images, the algorithm improves its success rate exponentially and has a negligible failure rate above 67 percent overlap. The experiments also quantify the reduction in errors as the model complexities increase. Final registration errors less than a pixel are routinely achieved. The speed, accuracy, and ability to handle small overlaps compare favorably with retinal image registration techniques published in the literature.
Enhanced differential super class-AB OTA
(2017)

A fully differential Super Class AB Operational Transconductance Amplifier (OTA) is presented. To provide additional dynamic current boosting and increased gain-bandwidth product (GBW), not only adaptive biasing techniques has been applied at the differential input stage, but also Local Common Mode Feedback (LCMFB). Additionally, Quasi Floating Gate (QFG) transistors are employed, enhancing the performance of the amplifier. The OTA has been fabricated in a standard 0.5-μm CMOS process. Simulation results yield a slew rate improvement by factor 86 and a GBW enhancement by factor 16 when compared to the class A OTA while driving the same 70-pF load. Supply voltages are ±1-V and the value of the quiescent current is 10 μA. The overhead in terms of quiescent power, silicon area, and noise level is small.
The neural correlates of maternal and romantic love
(2004)

Romantic and maternal love are highly rewarding experiences. Both are linked to the perpetuation of the species and therefore have a closely linked biological function of crucial evolutionary importance. Yet almost nothing is known about their neural correlates in the human. We therefore used fMRI to measure brain activity in mothers while they viewed pictures of their own and of acquainted children, and of their best friend and of acquainted adults as additional controls. The activity specific to maternal attachment was compared to that associated to romantic love described in our earlier study and to the distribution of attachment-mediating neurohormones established by other studies. Both types of attachment activated regions specific to each, as well as overlapping regions in the brain's reward system that coincide with areas rich in oxytocin and vasopressin receptors. Both deactivated a common set of regions associated with negative emotions, social judgment and 'mentalizing', that is, the assessment of other people's intentions and emotions. We conclude that human attachment employs a push-pull mechanism that overcomes social distance by deactivating networks used for critical social assessment and negative emotions, while it bonds individuals through the involvement of the reward circuitry, explaining the power of love to motivate and exhilarate.
Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images
(2002)

Linear registration and motion correction are important components of structural and functional brain image analysis. Most modern methods optimize some intensity-based cost function to determine the best registration. To date, little attention has been focused on the optimization method itself, even though the success of most registration methods hinges on the quality of this optimization. This paper examines the optimization process in detail and demonstrates that the commonly used multiresolution local optimization methods can, and do, get trapped in local minima. To address this problem, two approaches are taken: (1) to apodize the cost function and (2) to employ a novel hybrid global-local optimization method. This new optimization method is specifically designed for registering whole brain images. It substantially reduces the likelihood of producing misregistrations due to being trapped by local minima. The increased robustness of the method, compared to other commonly used methods, is demonstrated by a consistency test. In addition, the accuracy of the registration is demonstrated by a series of experiments with motion correction. These motion correction experiments also investigate how the results are affected by different cost functions and interpolation methods.
The rises and falls of disconnection syndromes.
(2005)

In a brain composed of localized but connected specialized areas, disconnection leads to dysfunction. This simple formulation underlay a range of 19th century neurological disorders, referred to collectively as disconnection syndromes. Although disconnectionism fell out of favour with the move against localized brain theories in the early 20th century, in 1965, an American neurologist brought disconnection to the fore once more in a paper entitled, 'Disconnexion syndromes in animals and man'. In what was to become the manifesto of behavioural neurology, Norman Geschwind outlined a pure disconnectionist framework which revolutionized both clinical neurology and the neurosciences in general. For him, disconnection syndromes were higher function deficits that resulted from white matter lesions or lesions of the association cortices, the latter acting as relay stations between primary motor, sensory and limbic areas. From a clinical perspective, the work reawakened interest in single case studies by providing a useful framework for correlating lesion locations with clinical deficits. In the neurosciences, it helped develop contemporary distributed network and connectionist theories of brain function. Geschwind's general disconnectionist paradigm ruled clinical neurology for 20 years but in the late 1980s, with the re-emergence of specialized functional roles for association cortex, the orbit of its remit began to diminish and it became incorporated into more general models of higher dysfunction. By the 1990s, textbooks of neurology were devoting only a few pages to classical disconnection theory. Today, new techniques to study connections in the living human brain allow us, for the first time, to test the classical formulation directly and broaden it beyond disconnections to include disorders of hyperconnectivity. In this review, on the 40th anniversary of Geschwind's publication, we describe the changing fortunes of disconnection theory and adapt the general framework that evolved from it to encompass the entire spectrum of higher function disorders in neurology and psychiatry.
EEG-correlated fMRI of human alpha activity
(2003)

Electroencephalography-correlated functional magnetic resonance imaging (EEG/fMRI) can be used to identify blood oxygen level-dependent (BOLD) signal changes associated with both physiological and pathological EEG events. Here, we implemented continuous and simultaneous EEG/fMRI to identify BOLD signal changes related to spontaneous power fluctuations in the alpha rhythm (8-12 Hz), the dominant EEG pattern during relaxed wakefulness. Thirty-two channels of EEG were recorded in 10 subjects during eyes-closed rest inside a 1.5-T magnet resonance (MR) scanner using an MR-compatible EEG recording system. Functional scanning by echoplanar imaging covered almost the entire cerebrum every 4 s. Off-line MRI artifact subtraction software was applied to obtain continuous EEG data during fMRI acquisition. The average alpha power over 1-s epochs was derived at several electrode positions using a Fast Fourier Transform. The power time course was then convolved with a canonical hemodynamic response function, down-sampled, and used for statistical parametric mapping of associated signal changes in the image time series. At all electrode positions studied, a strong negative correlation of parietal and frontal cortical activity with alpha power was found. Conversely, only sparse and nonsystematic positive correlation was detected. The relevance of these findings is discussed in view of the current theories on the generation and significance of the alpha rhythm and the related functional neuroimaging findings.
An evaluation framework for viable business models for m-commerce in the information technology sector
(2010)

This paper presents a study of the characteristics of viable business models in the field of Mobile Commerce (m-commerce). Mobility has given new dimensions to the way commerce works. All over the world various stakeholder organisations are consistently probing into the areas where m-commerce can be exploited and can generate revenue or value for them, even though some of those implementations are making the business environment more complex and uncertain. This paper proposes a viable business model evaluation framework, based on the VISOR model, which helps in determining the sustainability capabilities of a business model. Four individual cases were conducted with diverse organisations in the Information Technology sector. The four cases discussed dealt with mobile business models and the primary data was collected via semi structured interviews, supplemented by an extensive range of secondary data. A cross-case comparative data analysis was used to review the patterns of different viable business components across the four cases and, finally, the findings and conclusions of the study are presented.
Delving into adversarial attacks on deep policies
(2017)

Adversarial examples have been shown to exist for a variety of deep learning architectures. Deep reinforcement learning has shown promising results on training agent policies directly on raw inputs such as image pixels. In this paper we present a novel study into adversarial attacks on deep reinforcement learning polices. We compare the effectiveness of the attacks using adversarial examples vs. random noise. We present a novel method for reducing the number of times adversarial examples need to be injected for a successful attack, based on the value function. We further explore how re-training on random noise and FGSM perturbations affects the resilience against adversarial examples.